Digital imaging has been a tool used in numerous fields. With recent developments in digital imaging technology, and more recently with the proliferation of aerial drones, digital imaging use is expanding rapidly. Image collection, specifically focused on structure from motion (SfM) processing techniques, is performed widely using platforms such as an aerial drone equipped with consumer grade digital cameras. An important aspect of the current state of Structure from Motion (SfM) image collection can be characterized in terms of the impact that camera placement trajectories have on the delivered results.
Current UAV data collection strategies for rotor-wing quad-copters, and particularly those for fixed-wing platforms are largely based on the tried and tested flight plan styles developed for traditional aerial photography (e.g., orthophotography which are built up from sequential overlapping stereo pairs). With traditional orthophotography, the image data is acquired using regular parallel linear patterns, with overlapping images taken along a flight line forming an “image strip.” The parallel overlapping image strips used to survey areas form an “image block.”
Based on current research literature: “High resolution digital elevation models are increasingly produced from photographs acquired with consumer cameras, both from the ground and from manned aircraft as well as unmanned aerial vehicles (UAVs). However, although such digital elevation models may achieve centrimetric detail, they can also display systematic broad-scale error that restricts their wider use. Such errors, which in typical UAV data are expressed as a vertical “doming” of the digital elevation model (DEM) surface, result from a combination of near-parallel imaging directions and inaccurate correction of radial lens distortion. Using simulations of multi-image networks with near-parallel viewing directions, enabling camera self-calibration as part of the bundle adjustment process inherently leads to erroneous radial distortion estimates and associated digital elevation model error. This effect is relevant whether a traditional photogrammetric or newer structure-from-motion (SfM) approach is used; but errors are expected to be more pronounced in SfM-based digital elevation models, for which the use of control and check point measurements are typically more limited.”
Again, from current literature: “Specifically, a regular (often rectangular and evenly spaced) pattern of traditional linear, straight, parallel trajectories covering an area, corridor, or point of interest is used when capturing the necessary photographs. In particular, the traditional, regular, linear, parallel flight lines (used to collect UAV imagery in the recent times) contribute particularly to the well-established and studied systematic (doming) errors in the 3D point clouds derived from the SfM processing workflow—even when alternate flight lines are flown in opposing directions. Such flight paths for collecting images result in the production and propagation of systematic errors within the SfM workflow processes. These errors are termed the “dome” effect. This effect occurs even though emphasis has been placed on maintaining 60% to 90% axial (forward) overlap and 20% to 40% lateral (side-to-side) overlap for placement of images on-the-ground within the target area.”
A generally accepted consensus among researchers is that “convergent image capture along gently curved trajectories (e.g., circles) clearly mitigates SfM surface deformation in self-calibrating multi-image blocks.” However, such circular trajectories are neither efficient nor effective enough to cover every potential area or shape.
The traditional (straight-linear-parallel) trajectory scenario (a logical carryover from the longstanding traditional aerial photography industry) is generally applied for all types of SfM projects—for point and linear targets (landmark features), agricultural (rural and urban areas), and corridors (highways, pipelines, electric power transmission corridors, railroads, waterways, etc.). An exception to this heuristic holdover practice might relate to a) cityscape areas with tall buildings or b) isolated tall towers where circular trajectories might be used. Of course, emphasis is always placed on capturing all available data in the shortest possible time frame—leaving the SfM software/workflow with the bulk of the production (problem solving) effort, particularly in the instance of using either manned or unmanned aircraft to collect the photographs where duration of flight is a major concern.
FIGS. 1A-1E depict specific, recent traditional, straight, parallel flight line patterns for unmanned aerial vehicles (UAVs). The processed results from such obtained images suffer from doming effects/errors. FIG. 1A shows traditional, longer, straight, parallel UAV flight lines for collecting digital images (digital aerial photographs) over a vineyard for the purpose of constructing 3D rendered point cloud models of the scene along with the associated orthophotography and DEM using SfM processing techniques.
FIG. 1B shows traditional, longer, straight, parallel UAV flight lines for collecting digital images (digital aerial photographs) over a cultivated agricultural land for the purpose of constructing 3D rendered point cloud models of the scene along with the associated orthophotography and DEM using SfM processing techniques.
FIG. 1C shows traditional, longer, straight, parallel UAV flight lines for collecting digital images (digital aerial photographs) over developed rural and/or urban land areas for the purpose of constructing 3D rendered point cloud models of the scene along with the associated orthophotography and DEM using SfM processing techniques.
FIG. 1D shows traditional, longer, straight, parallel UAV flight lines for collecting digital images (digital aerial photographs) over populated urban land areas for the purpose of constructing 3D rendered point cloud models of the scene along with the associated orthophotography and DEM using SfM processing techniques.
FIG. 1E shows traditional, longer, straight, parallel UAV flight lines for collecting digital images (digital aerial photographs) over rural and/or urban corridors for the purpose of constructing 3D rendered point cloud models of the scene along with the associated orthophotography and DEM using SfM processing techniques.
Thus, current methods of deploying cameras (on the ground or in the air) for capturing digital imagery (overlapping digital photographs) for the construction of SfM 3D topographical models and associated orthophotography generally use more traditional, straight, linear, parallel trajectories as shown in FIGS. 1A-1E. It has been demonstrated repeatedly that such trajectories have a strong inherent tendency to introduce systematic error (doming effect) into the delivered SfM products—often rendering the delivered products less than optimally suited for the intended purpose.
Thus, there is a need for a method to produce trajectories for image gathering that avoids the doming deformation effect. There is a need for a system that allows the generation of flight paths to maximize efficient errorless gathering of images over areas and corridors.